While virtual reality (VR) excels at simulating physical environments, its effectiveness for training complex interpersonal skills is limited by a lack of psychologically plausible virtual humans. This gap is particularly critical in medical education, where communication is a core clinical competency. This paper introduces a framework that integrates large language models (LLMs) into immersive VR to create medically coherent virtual patients with distinct, consistent personalities, based on a modular architecture that decouples personality from clinical data. We evaluated the system in a mixed-methods, within-subjects study with licensed physicians conducting simulated consultations. Results suggest that the approach is feasible and perceived as a rewarding and effective training enhancement. Our analysis highlights key design principles, including a "realism-verbosity paradox" and the importance of challenges being perceived as clinically authentic to support learning.

翻译：尽管虚拟现实（VR）在模拟物理环境方面表现卓越，但其在培训复杂人际技能方面的有效性，因缺乏心理层面可信的虚拟人而受到限制。这一不足在医学教育中尤为关键，因为沟通能力是核心临床胜任力之一。本文提出一种框架，将大型语言模型（LLM）整合至沉浸式VR中，基于一种将人格特质与临床数据解耦的模块化架构，创建具有鲜明且一致人格的医学逻辑自洽的虚拟患者。我们通过一项混合方法、被试内设计的实证研究对该系统进行了评估，研究由执业医师执行模拟问诊。结果表明，该方法是可行的，并被参与者视为一种有价值且有效的培训增强手段。我们的分析揭示了关键设计原则，包括“真实感-冗余度悖论”，以及挑战需被感知为临床真实以支持学习的重要性。